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Big Data and Hadoop
Big Data and Hadoop training Unlike traditional systems, Big Data and Hadoop enables multiple types of analytic workloads to run on the same data, at the same time, at massive scale on industry-standard hardware.myTectra Big Data and Hadoop training is designed to help you become a expert Hadoop developer. myTectra offers Big Data Hadoop Training in Bangalore using Class Room. myTectra offers Live Online Big Data and Hadoop training Globally.
Chapter 1: Understanding Big Data and Hadoop
Big Data
Limitations and Solutions of existing Data Analytics Architecture
Hadoop
Hadoop Features
Hadoop Ecosystem
Hadoop 2.x core components
Hadoop Storage: HDFS
Hadoop Processing: MapReduce Framework
Hadoop Different Distributions
Chapter 2:Hadoop Architecture and HDFS
Hadoop 2.x Cluster Architecture - Federation and High Availability
A Typical Production Hadoop Cluster
Hadoop Cluster Modes
Common Hadoop Shell Commands
Hadoop 2.x Configuration Files
Single node cluster and Multi node cluster set up Hadoop Administration
Chapter 3: Hadoop MapReduce Framework
MapReduce Use Cases
Traditional way Vs MapReduce way
Why MapReduce
Hadoop 2.x MapReduce Architecture
Hadoop 2.x MapReduce Components
YARN MR Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
Demo on MapReduce
Input Splits
Relation between Input Splits and HDFS Blocks
MapReduce: Combiner & Partitioner
Demo on de-identifying Health Care Data set
Demo on Weather Data set
Chapter 4: Advanced MapReduce
Counters
Distributed Cache
MRunit
Reduce Join
Custom Input Format
Sequence Input Format
Xml file Parsing using MapReduce
Chapter 5: Pig
About Pig
MapReduce Vs Pig
Pig Use Cases
Programming Structure in Pig
Pig Running Modes
Pig components
Pig Execution
Pig Latin Program
Data Models in Pig
Pig Data Types
Shell and Utility Commands
Pig Latin : Relational Operators
File Loaders, Group Operator
COGROUP Operator
Joins and COGROUP
Union
Diagnostic Operators
Specialized joins in Pig
Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution )
Pig Streaming
Testing Pig scripts with Punit
Aviation use case in PIG
Pig Demo on Healthcare Data set
Chapter 6:Hive
Hive Background
Hive Use Case
About Hive
Hive Vs Pig
Hive Architecture and Components
Metastore in Hive
Limitations of Hive
Comparison with Traditional Database
Hive Data Types and Data Models
Partitions and Buckets
Hive Tables(Managed Tables and External Tables)
Importing Data
Querying Data
Managing Output
Hive Script
Hive UDF
Retail use case in Hive
Hive Demo on Healthcare Data set
Chapter 7:Advanced Hive and HBase
Hive QL: Joining Tables
Dynamic Partitioning
Custom Map/Reduce Scripts
Hive Indexes and views Hive query optimizers
Hive : Thrift Server, User Defined Functions
HBase: Introduction to NoSQL Databases and HBase
HBase v/s RDBMS
HBase Components
HBase Architecture
Run Modes & Configuration
HBase Cluster Deployment
Big Data
Limitations and Solutions of existing Data Analytics Architecture
Hadoop
Hadoop Features
Hadoop Ecosystem
Hadoop 2.x core components
Hadoop Storage: HDFS
Hadoop Processing: MapReduce Framework
Hadoop Different Distributions
Chapter 2:Hadoop Architecture and HDFS
Hadoop 2.x Cluster Architecture - Federation and High Availability
A Typical Production Hadoop Cluster
Hadoop Cluster Modes
Common Hadoop Shell Commands
Hadoop 2.x Configuration Files
Single node cluster and Multi node cluster set up Hadoop Administration
Chapter 3: Hadoop MapReduce Framework
MapReduce Use Cases
Traditional way Vs MapReduce way
Why MapReduce
Hadoop 2.x MapReduce Architecture
Hadoop 2.x MapReduce Components
YARN MR Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
Demo on MapReduce
Input Splits
Relation between Input Splits and HDFS Blocks
MapReduce: Combiner & Partitioner
Demo on de-identifying Health Care Data set
Demo on Weather Data set
Chapter 4: Advanced MapReduce
Counters
Distributed Cache
MRunit
Reduce Join
Custom Input Format
Sequence Input Format
Xml file Parsing using MapReduce
Chapter 5: Pig
About Pig
MapReduce Vs Pig
Pig Use Cases
Programming Structure in Pig
Pig Running Modes
Pig components
Pig Execution
Pig Latin Program
Data Models in Pig
Pig Data Types
Shell and Utility Commands
Pig Latin : Relational Operators
File Loaders, Group Operator
COGROUP Operator
Joins and COGROUP
Union
Diagnostic Operators
Specialized joins in Pig
Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution )
Pig Streaming
Testing Pig scripts with Punit
Aviation use case in PIG
Pig Demo on Healthcare Data set
Chapter 6:Hive
Hive Background
Hive Use Case
About Hive
Hive Vs Pig
Hive Architecture and Components
Metastore in Hive
Limitations of Hive
Comparison with Traditional Database
Hive Data Types and Data Models
Partitions and Buckets
Hive Tables(Managed Tables and External Tables)
Importing Data
Querying Data
Managing Output
Hive Script
Hive UDF
Retail use case in Hive
Hive Demo on Healthcare Data set
Chapter 7:Advanced Hive and HBase
Hive QL: Joining Tables
Dynamic Partitioning
Custom Map/Reduce Scripts
Hive Indexes and views Hive query optimizers
Hive : Thrift Server, User Defined Functions
HBase: Introduction to NoSQL Databases and HBase
HBase v/s RDBMS
HBase Components
HBase Architecture
Run Modes & Configuration
HBase Cluster Deployment
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